Reposted by valeo.aiAndrei Bursuc @abursuc.bsky.social · 06/10/2026Meet 𝚆𝚑𝚎𝚛𝚎-𝙾𝙿𝙳: a simple strategy to generate synthetic data and tasks for MLLM on-policy self-distillation to improve visual perception. Counting is a super useful skill to have and giving the teacher hints about where to look makes this work. 151
Reposted by valeo.aiSophia Sirko-Galouchenko 🇺🇦 @ssirko.bsky.social · 05/10/20261/n 🚀 New paper 𝚆𝚑𝚎𝚛𝚎-𝙾𝙿𝙳 In on-policy self-distillation, the student learns from a privileged version of itself. In recent methods, privilege often comes from a better view of the image. What if, instead of a better view, the teacher knew where to look? 👀 2163
Reposted by valeo.aiELLIOT Project @elliot-eu.bsky.social · 02/10/2026How can autonomous driving AI become more efficient? 🚗 🔬We highlight DrivoR by @valeoai.bsky.social, a transformer-based approach that compresses multi-camera information into compact scene representations while supporting safer, more adaptive driving decisions. Read it👉 arxiv.org/pdf/2601.05083 011
Reposted by valeo.aiELLIOT Project @elliot-eu.bsky.social · 22/09/2026We were in Barcelona for the Summer School on Autonomous Driving! 🚗 The event explored how AI can support safer, smarter mobility in complex real-world environments. It was great to see so much curiosity, technical exchange and energy in Barcelona. 🤝 042
Reposted by valeo.aiAndrei Bursuc @abursuc.bsky.social · 16/09/2026We start the 2nd #ssad2026 day with @kashyap7x.bsky.social talking about: Making progress measurable - Evaluation and benchmarking of driving policies 1124
Reposted by valeo.aiAndrei Bursuc @abursuc.bsky.social · 16/09/2026Fatma Güney up next speaking on Learning compact state representations for reliable autonomous driving #ssad2026 181
Reposted by valeo.aiAndrei Bursuc @abursuc.bsky.social · 16/09/2026The Carla OG German Ros talks about Synthetic data workflows to accelerate AV #ssad2026 151
Reposted by valeo.aiAndrei Bursuc @abursuc.bsky.social · 15/09/2026José M. Alvarez speaks of End-to-end models for autonomous driving - from direct prediction to counterfactual deliberation on the edge #ssad2026 161
Reposted by valeo.aiAndrei Bursuc @abursuc.bsky.social · 15/09/2026Maximilian Igl on stage talking about Reinforcement Learning for Autonomous Driving #ssad2026 His slides are available online www.maximilianigl.com/blog/rl-for-... 191
Reposted by valeo.aiTetiana Martyniuk🇺🇦@ ICIP’26🇫🇮 @t-martyniuk.bsky.social · 16/09/2026(1/n) Can older lidar SSC models catch up with recent SOTA just by getting better inputs? At #ICIP2026 today and in our new journal extension, we add two cheap priors to existing networks, with no redesign. 📍 Poster #2752, Park Hall, 14:00–16:00 🇫🇮 🧵👇 182
valeo.ai @valeoai.bsky.social · 07/09/2026In Malmö for #ECCV2026? Come find us at the #EmergingAD workshop: 🎤 Oral: September 8, 2026, 11:00, room Malmömässan E2 🖼️ Poster: 14:30-15:30, Malmömässan Exhibit Hall, board #384 Program: emerging-ad.github.ioemerging-ad.github.ioWorkshop on Emerging Behaviors in Embodied AI for Achieving Robust Autonomy 010
valeo.ai @valeoai.bsky.social · 07/09/2026Work by @yuanyinnn.bsky.social, E. Ramzi, M. Lafon, V. Charraut, V. Bares, Y. Xu, @eloizablocki.bsky.social, A. Boulch, T. Buhet, @abursuc.bsky.social, @matthieucord.bsky.social 📄 Paper: arxiv.org/abs/2607.26005 🌐 Project page: valeoai.github.io/Pictura #AutonomousDriving #ReinforcementLearningarxiv.orgPictura: Perspective-View Self-Play at Scale for DrivingSelf-play in simulation produces robust driving policies at scale. Demonstrations of such behavior have been made using privileged vectorized observations such as exact poses and velocities, even for ... 120
valeo.ai @valeoai.bsky.social · 07/09/2026So, can a car learn to drive by playing against itself, cameras only? In simulation, yes. You don't need to assume perception is solved for self-play to work in driving. Train the way you deploy: from pixels. What would you build with a simulator that renders 2M images/s? 110
valeo.ai @valeoai.bsky.social · 07/09/2026How Alberti performs ✅ Approaches its privileged counterpart in-domain in most metrics ✅ Zero-shot transfer to Waymo Open Motion Dataset layouts re-rendered in Pictura ✅ On those unseen layouts it actually BEATS the privileged agents 110
valeo.ai @valeoai.bsky.social · 07/09/2026With Pictura's throughput we trained Alberti: to our knowledge the first large-scale driving self-play policy learned directly from perspective images, no privileged observations, just plain PPO. Training run: 50B agent steps. Roughly 35 million km of driving. 130
valeo.ai @valeoai.bsky.social · 07/09/2026So we made it cheap. Meet Pictura: a GPU-accelerated multi-agent driving simulator that renders each agent's egocentric view at every single step. Up to 500K agent-steps per second. That's 2M images per second. On ONE H100. 240
valeo.ai @valeoai.bsky.social · 07/09/2026Our fix removes it for real: no teacher, just self-play directly from camera views. Why has nobody done this at scale? Because rendering every agent's camera view at every step is brutally expensive. 110
valeo.ai @valeoai.bsky.social · 07/09/2026The usual patch hides the shortcut rather than removing it. Distillation: train a "privileged" policy with perfect information, then teach a camera-only student to imitate it. Problem: the student copies decisions its own view cannot justify, reacting to cars it cannot see. 120
valeo.ai @valeoai.bsky.social · 07/09/2026Can a car learn to drive by playing against itself, using only its cameras? Self-play driving agents have relied on a shortcut: perfect knowledge of every vehicle's position and speed, even the ones they can't see. A real car never gets that. We take the shortcut away. 🧵 1154
valeo.ai @valeoai.bsky.social · 07/09/2026How Far Can 5,500 Hours of Driving Take You? A Scaling Law Analysis of Video Diffusion Models tl;dr: we present a systematic scaling-law study of video diffusion models (1M-9B params) on driving data by: V. Besnier, A.Q. Cao & team 📄: arxiv.org/abs/2608.28404 010
valeo.ai @valeoai.bsky.social · 07/09/2026Pictura: Perspective-View Self-Play at Scale for Driving tl;dr: a GPU-accelerated multi-agent driving simulator that renders each agent's egocentric view at every step. by: Y. Yin, E. Ramzi, M. Lafon & team 📄: arxiv.org/abs/2607.26005 110
valeo.ai @valeoai.bsky.social · 07/09/2026CoReDi: Coevolving Representations in Joint Image-Feature Diffusion tl;dr: learning the feature space in joint diffusion image generation models is super handy by: T. Kouzelis, S. Gidaris, N. Komodakis 📄: arxiv.org/abs/2604.17492 x.com/ThKouz/statu... 110
valeo.ai @valeoai.bsky.social · 07/09/2026SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization tl;dr: novel tokenizer with high compression rate for images in autoencoders by: T. Vallaeys, J. Verbeek, M. Cord 📄: arxiv.org/abs/2510.04961 x.com/webalorn/sta... 110
valeo.ai @valeoai.bsky.social · 07/09/2026Re2Pix: Representations Before Pixels: Semantics-Guided Hierarchical Video Prediction tl;dr: forecast in VFM semantic space first, synthesize pixels second by: E. Karypidis, S. Gidaris, N. Komodakis 📄: arxiv.org/abs/2604.11707 x.com/K_Sta8is/sta... 110
valeo.ai @valeoai.bsky.social · 07/09/2026We’ll be @eccv.bsky.social to present our most recent research! We’ll present three papers in the main conference & two in the Workshops. We will also speak in two workshops & one tutorial. Here is a quick overview on the works we’ll resent in Malmö #eccv2026 valeoai.github.io/posts/eccv-2... 162
valeo.ai @valeoai.bsky.social · 27/07/2026Multiple #ECCV2026 outstanding reviewer awards at @valeoai.bsky.social! Congrats to A. Boulch, @abursuc.bsky.social, @anhquancao.bsky.social, N. Samet, @t-martyniuk.bsky.social, Y. Xu, to our visiting PhD student E. Karypidis, and to our alumnus @gillespuy.bsky.social! 0102
Reposted by valeo.aiELLIOT Project @elliot-eu.bsky.social · 23/07/2026How can AI better understand 3D driving scenes? 🚗 In this ELLIOT Research Spotlight 🔬, we highlight LOSC from @valeoai.bsky.social: a method for open-vocabulary LiDAR segmentation that helps assign meaning to 3D point clouds, beyond fixed object categories. Paper + code in comments 👇 122
valeo.ai @valeoai.bsky.social · 06/07/2026Everything is out 👇 🌐 Website: alan-lanfeng.github.io/rdm/ 📄 Paper: arxiv.org/abs/2607.02375 🤗 1-step demo: huggingface.co/spaces/epfl-... 💻 Code: github.com/vita-epfl/RDM Led by Lan Feng from EPFL, with Wuyang Li, @eloizablocki.bsky.social, @matthieucord.bsky.social, Alexandre Alahialan-lanfeng.github.ioRepresentation Distribution Matching for One-Step Visual GenerationOne step from real. State-of-the-art one-step generation by matching feature distributions, no online teacher, no adversary, no trajectory. 020
valeo.ai @valeoai.bsky.social · 06/07/2026On text-to-image, matching image distributions isn't enough: a model can produce beautiful images while ignoring every prompt. The fix is simply to match the joint (image, caption) law, and prompt fidelity becomes part of the distribution itself. 100
valeo.ai @valeoai.bsky.social · 06/07/2026The recipe also post-trains existing multi-step models into one-step generators. FLUX.2 [klein], a 4-step sampler, becomes 1-step in 90 GPU-hours, and beats its 4-step teacher on GenEval and PickScore. 4x faster, at matched quality 110
valeo.ai @valeoai.bsky.social · 06/07/2026Why 10 encoders? Because we find that matching only one (or a few) can be gamed 100
valeo.ai @valeoai.bsky.social · 06/07/2026Two things make it work: --> Generated side: batches must be huge (>2k+ samples per step, stored w/ gradient caching) --> Real side: all 1.28M ImageNet images compressed once into a frozen Nyström reference 100
valeo.ai @valeoai.bsky.social · 06/07/2026The whole recipe: Generate a large batch of images, embed them with 10 frozen pretrained encoders (DINO, SigLIP, MAE...), and align their feature distribution to match ImageNet's. The loss? A simple MMD. 110
valeo.ai @valeoai.bsky.social · 06/07/2026New state of the art in one-step image generation 🏆 One forward pass. The method is so simple it's almost unsettling: no teacher, no discriminator, no denoising trajectory 🧵 151
valeo.ai @valeoai.bsky.social · 03/07/20262/ DRIV-EX: Counterfactual Explanations for Driving LLMs Counterfactual explanations: the minimal semantic edit that flips an LLM driving planner's decision. They can expose unexpected biases 📄 arxiv.org/abs/2603.00696 A. Cardiel, @eloizablocki.bsky.social, E. Ramzi, E. Gaussier 020
valeo.ai @valeoai.bsky.social · 03/07/20261/ Multiple Choice Learning of Low-Rank Adapters for Language Modeling TL;DR: LoRA heads + Winner-Takes-All loss → diverse plausible outputs 📄 arxiv.org/abs/2507.10419 V. Letzelter, H. Malard, M. Fontaine, G. Richard, S. Essid, @abursuc.bsky.social, @ptrkprz.bsky.social 110
valeo.ai @valeoai.bsky.social · 03/07/2026valeo.ai is heading to #ICML2026 and #ACL2026 with two papers: - diverse LLM decoding with LoRA-MCL, - counterfactual explanations for driving LLMs with DRIV-EX 🧵👇 131
Reposted by valeo.aiAndrei Bursuc @abursuc.bsky.social · 19/06/2026The #WorldEngine tech report is now up! It's our post-training framework to deal w/ scarcity of long-tail safety-critical scenarios. Post-training strategies for autonomous driving are rarely tackled and discussed in the open. We hope that this will open more this area. arxiv.org/abs/2606.19836 1132
Reposted by valeo.aiELLIOT Project @elliot-eu.bsky.social · 09/06/2026Missed the deadline? You’re in luck! 🚨 👉 Deadline Extended: Friday, 12 June! Last call to dive into robust, multimodal AI for autonomous driving! 🏎️✨ 👇 Apply before Friday! pages.cvc.uab.es/ssadriving/ @ellis.eu @valeoai.bsky.social @uab.catpages.cvc.uab.esELLIS Summer School on Autonomous Driving — Barcelona 2026An official ELLIS Summer School — four intensive days in Barcelona where world-class researchers and the next generation of scientists converge to push the boundaries of perception, world models, end-to-end driving, and beyond. 043
valeo.ai @valeoai.bsky.social · 03/06/2026Great event at CVPR in Paris! 🎉 The @valeoai.bsky.social team got to present several posters and had a lot of fun discussions with the community. Thanks to everyone who stopped by! 161
valeo.ai @valeoai.bsky.social · 02/06/20262/ PPT — pseudo-labeled motion forecasting Pretrain on detecor+tracker trajectories → cross-domain gains. 📄 arxiv.org/abs/2412.06491 💻 github.com/valeoai/ppt Y. Xu, @yuanyinnn.bsky.social, @eloizablocki.bsky.social, @tuanhungvu.bsky.social, @alexandreboulch.bsky.social, M. Cord 010
valeo.ai @valeoai.bsky.social · 02/06/20261/ R3DPA: LiDAR scene generation via image-prior transfer Aligning gen features w/ self-supervised 3D, transferring RGB priors → SOTA on KITTI-360 📄 arxiv.org/abs/2601.07692 💻 github.com/valeoai/R3DPA N. Sereyjol-Garros, E. Kirby, V. Besnier, N. Samet 100
valeo.ai @valeoai.bsky.social · 02/06/2026The team valeo.ai is at #ICRA2026 in Vienna! 2 papers: one on LiDAR scene generation w/ RGB-pretrained priors, and one on pretraining motion forecasting models from pseudo-labeled trajectories. 110
Reposted by valeo.aiELLIOT Project @elliot-eu.bsky.social · 01/06/2026The schedule for the ELLIS Summer School on Autonomous Driving (SSAD 2026) is LIVE! 🏎️✨ 4 days of top-tier AI & robotics research in Barcelona, proudly supported by ELLIOT. ⏳ 7 days left to apply! Secure your spot: 🔗 pages.cvc.uab.es/ssadriving/ @ellis.eu @valeoai.bsky.social @uab.cat 072
valeo.ai @valeoai.bsky.social · 01/06/2026ICM: Attention, May I Have Your Decision? Localizing Generative Choices in Diffusion Models tl;dr: a steering method w/ targeted interventions to a subset of layers to address ambiguous concepts by: K. Zaleska, L. Popek, M. Wysoczanska, K. Deja 📄: arxiv.org/abs/2604.06052 010
valeo.ai @valeoai.bsky.social · 01/06/2026LAM3C: 3D sans 3D Scans: Scalable Pre-training from Video-Generated Point Clouds tl;dr: 3D self-supervised learning without actual 3D scans but from internet videos and VGGT reconstructions by: R. Yamada, K. Ide, Y. Fukuhara, H. Kataoka, G. Puy, . Bursuc, Y. Asano 📄: arxiv.org/abs/2512.23042 110
valeo.ai @valeoai.bsky.social · 01/06/2026LiDAS: Lighting-driven Dynamic Active Sensing for Nighttime Perception tl;dr: HD headlights as active sensors to adaptively focus light in key areas to boost perception in nighttime conditions by: S. de Moreau, A. Bursuc, H. El-Idrissi, F. Moutarde 📄: arxiv.org/abs/2512.08912 110
valeo.ai @valeoai.bsky.social · 01/06/2026StableMTL: Repurposing Latent Diffusion Models for Multi-Task Learning from Partially Annotated Synthetic Datasets tl;dr: denoising framework w/ task encoding, per-task conditioning & a tailored training scheme by: A.Q. Cao, I. Lopes, R. de Charette 📄: arxiv.org/abs/2506.08013 110
valeo.ai @valeoai.bsky.social · 01/06/2026MAD: Motion Appearance Decoupling for efficient Driving World Models tl;dr: efficiently adapt generalist video diffusion models into controllable driving world models w/ minimal supervision by: A. Rahimi, V. Gerard, E. Zablocki, M. Cord, A. Alahi 📄: arxiv.org/abs/2601.09452 130